{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_purchase_amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>report_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>32488348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-02</th>\n",
       "      <td>29037390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-03</th>\n",
       "      <td>27270770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-04</th>\n",
       "      <td>18321185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-05</th>\n",
       "      <td>11648749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-27</th>\n",
       "      <td>302194801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-28</th>\n",
       "      <td>245082751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-29</th>\n",
       "      <td>267554713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-30</th>\n",
       "      <td>199708772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>275090213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             total_purchase_amt\n",
       "report_date                    \n",
       "2013-07-01             32488348\n",
       "2013-07-02             29037390\n",
       "2013-07-03             27270770\n",
       "2013-07-04             18321185\n",
       "2013-07-05             11648749\n",
       "...                         ...\n",
       "2014-08-27            302194801\n",
       "2014-08-28            245082751\n",
       "2014-08-29            267554713\n",
       "2014-08-30            199708772\n",
       "2014-08-31            275090213\n",
       "\n",
       "[427 rows x 1 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from pylab import *\n",
    "mpl.rcParams['font.sans-serif']=['SimHei']\n",
    "funddata = pd.read_csv(\"../data/user_balance_table.csv\")\n",
    "# 将目标列读取为日期型\n",
    "funddata['report_date'] = pd.to_datetime(funddata['report_date'], format='%Y%m%d') \n",
    "# 对相同日期的资金申购量进行统计  \n",
    "combine = funddata.groupby(['report_date']).agg({'total_purchase_amt': sum}) \n",
    "combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['axes.unicode_minus']=False\n",
    "combine.plot(legend=False) \n",
    "plt.title('时序图')\n",
    "plt.show()\n",
    "plot_acf(combine)\n",
    "plt.title('自相关图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             total_purchase_amt\n",
      "report_date                    \n",
      "2014-03-01            362865580\n",
      "2014-03-02            276202230\n",
      "2014-03-03            505305862\n",
      "2014-03-04            524146340\n",
      "2014-03-05            454295491\n",
      "...                         ...\n",
      "2014-07-27            151406251\n",
      "2014-07-28            371762756\n",
      "2014-07-29            228093046\n",
      "2014-07-30            209917272\n",
      "2014-07-31            191728916\n",
      "\n",
      "[153 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 截取平稳部分数据\n",
    "smooth = combine['2014-3':'2014-7']  \n",
    "print(smooth)\n",
    "smooth .plot(legend=False)  # 截取数据的时序图\n",
    "plt.title('截取数据时序图')\n",
    "plt.show()  # 截取的训练数据自相关图\n",
    "plot_acf(smooth )\n",
    "plt.title('截取数据自相关图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 周期性差分\n",
    "diffresult = smooth .diff(7)\n",
    "diffresult.plot(legend=False)\n",
    "diffresult = diffresult['2014-03-08':'2014-07-31']  # 需要进行数据的提取\n",
    "plt.title('差分时序图')\n",
    "plt.show()  # 差分时序图\n",
    "plot_acf(diffresult)  # 差分数据自相关图\n",
    "plt.title('差分自相关图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "差分序列的ADF检验结果为： (-3.949706067525637, 0.0016980133887242394, 14, 131, {'1%': -3.481281802271349, '5%': -2.883867891664528, '10%': -2.5786771965503177}, 5077.645620248397)\n",
      "差分序列的白噪声检验结果为： (array([26.50345968]), array([2.63066558e-07]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\stats\\diagnostic.py:524: FutureWarning: The value returned will change to a single DataFrame after 0.12 is released.  Set return_df to True to use to return a DataFrame now.  Set return_df to False to silence this warning.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# ADF检验\n",
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "print('差分序列的ADF检验结果为：', ADF(diffresult))\n",
    "# 白噪声检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "print('差分序列的白噪声检验结果为：', acorr_ljungbox(diffresult, lags=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析与建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\kalmanf\\kalmanfilter.py:220: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  Z_mat, R_mat, T_mat)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:689: RuntimeWarning: overflow encountered in exp\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:689: RuntimeWarning: invalid value encountered in true_divide\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:690: RuntimeWarning: overflow encountered in exp\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:690: RuntimeWarning: invalid value encountered in true_divide\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:716: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  invmacoefs = -np.log((1-macoefs)/(1+macoefs))\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:670: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  (1-a**2)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:672: RuntimeWarning: divide by zero encountered in arctanh\n",
      "  invarcoefs = 2*np.arctanh(params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC (3, 4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "# 模型定阶\n",
    "train_results = sm.tsa.arma_order_select_ic(smooth, ic=['bic'], trend='nc', max_ar=5, max_ma=5)\n",
    "print('BIC', train_results.bic_min_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEGCAYAAABvtY4XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAdqklEQVR4nO3df5wcdZ3n8dd7ZjJhSPgVGKJxgTxYckj8kZiNIoo6Iqjs7boeuiur697d6iNyx+rt6d2Jit4dx7K7nA9vV11Ysxc8D3/ssa7LiYqip6zggZpg8CEoF1YTAhoJJCGEH/nR/bk/qjrT0+me7qmenq7+zvv5eMxjqruqq75dXfX+futb1V2KCMzMLD1D/S6AmZn1hgPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngbKJJ+W9IfSDpD0slNxr9Q0r+oe/xbki5pM8/nSfpEB8t+i6T/OcPyni7p+LrHz5e0dCbzMCtqpN8FMGtG0quATwCH8qdGgN8DjgN+FTgJeJWk10ZESBoFDgIPAv9N0vURUQH+HXBlPs/RiDjQZHHPAY7uoFgH8z8kvRy4CfhZ3fiVwJKI2Ff33JuBFwBvyB9fCdwB/EnD+70rf4/NyldzSkS4crCOOeCtrG4lC8zNwFqyYK0AzyI78vww8ChwKrAN+HNgNVDNX79X0r3AfuCDkj5EVlm8orYASb8AfgkclT3U5rrljwMTEbFF0gLgWGAhMCzpOEDAtyLi9XXz28pkBTCcl/Nq4NN5K34RcBbwxnyeERG1CuwgcFFEbJX0EuCDwK9H/k1ESSPA1kJr0uYtB7yVUkRUJJ0I7I2IJwEknQ9MAK/J/58KLAWuAi4FRiKiFrC/BF5aa7HnLfxKk+WsbrZ8SbczWVmsBv4aeAbZPrMK+HSLoteW8Urgo0y2yH+SD+8CvkcW/lcBf5OPr5X7uHxZTwHfl/Rs4L1MPZox64gD3srsYmC5pPuBx4GvA8Nk3TD/PCK2Q9b0BpYAX5W0n6wbZxj4ZjYKAaPAFWTdKjVjkja2WPaZ5GEdEd8HVku6BXggIt4uaS1wecPrn1kbiIhvkB2BkJfxSuD+iPgf07zf4bx8u4DbgDuBtwPX4vNlVoAD3kpJ0jHAHwEvBU4E/n1E/AdJZwI3AG+WdDbwXOBCYFtEvDB/7WXAaERc0TDPEUkjdd0i+yJibYvl397weBnwcuDe/ETrtcA/NOmi6fT9KS/j/rqnK8A7gUeA2/P3/08joirJAW8z5oC3snoT8CWy7ol/BP5R0j3AXrKumZ8BPwa2k7Xe/07SIbJ+8jOBH0p6XcM8h4H3AzcXKM9lwDeAHWQBPAG8sqHfflltQNJNZEcStW6eZwMLJP1hbRJgoaRV+clggFOA/5QPfxb4DvAOSeuB2uvMOib/mqSVUd5irfVTvxtYQ9ZKf0zS14BrgD8D/ktEfKbudeuBPcBlEVGte35hQ2sZSXuA+1sU4UzgeflJzzVkgXsFcF59F02TFvwZdUcItecXk1VGB4HXRMSWJu/3drKrhA4CPwfemr/v9cBngKeB+yJieeu1ZjaVW/BWSnk4V/NW+80R8cO60T8BPkUWlt+FLMDJrqR5BvBPgNslLSfru98JHCXpJQ2XSXbaRfMj4I1krfAZya+W+SzZSdKNZOcFfjcibm+cNH/fD0l6L9lllX9H1g30WH4VjWa6fJvfHPBWWpLeAPwx8MK8z/oVZJcPPgDcS3YC9iyyrpB1ZCdhL6q1oCV9HLgzIlpd8XJsm5OstdA9APwoX1YtZIdo3kUzAhzKy3s+8F+BmyKidi3+pcCXJN1K1jK/KSKeBhbUZhIRfybpk8Bb8nLck4/y/moz4g3GSknSy4CPk51k/IWk9wC/AXwoIm6TdBLwOeBs4BLgPWTXs/8gv5IGsksoXy3pj8iCeTHwiojYkY8/0KYFv7Dh6YVkV+OQ/292HfwCsu6UL5BVPJdExJ21aSLii5JWAh8C/i3wv/NRI8BXJB3xRaf8Gv7aNGYdcx+8lZakYyLi8TbTrAR+HHOwIUsaI7vy5bH8uvqjI2JPi2mPylvmnc77RGBP3QlXs6454M3MEuVra83MEuWANzNLVGlO2px00kmxfPnyfhfDzGygbNq06ZGIGG82rjQBv3z5cjZubHXFmpmZNSNpW6tx7qIxM0uUA97MLFEOeDOzRDngzcwS5YA3M0tUaa6iKaJSDW6972Hu+flenrPsWCbOPJnhIf/gnpkZdBHwkpYCn4+Il7UYv4DsB5eWABsi4rqiy2qmUg3euuG7bN6+h6cOVBgbHWb1Kcdz/dvOdsibmVGwi0bSCWS/x71omsneCWyKiJeS3UX+mCLLauXW+x5m8/Y9PHmgQgBPHqiwefsebr3v4dlcjJnZwCraB18hu6Xa3mmmmSC7dybAt4EjfpZV0jpJGyVt3Llz54wKcM/P9/LUgak/vPfUgQr3/ny6IpmZzR+FAj4i9kbEY20mWwQ8lA/vIvtt7sb5rI+ItRGxdny86TdtW3rOsmMZGx2e8tzY6DArlx07o/mYmaWql1fR7APG8uHFs72siTNPZvUpx6PKAYgqR+d98BNnnjybizEzG1i9DPhNwLn58Cpg62zOfHhIXP+2sxnfchPHP/gdPva7L/AJVjOzOrNymaSk84CVEfHxuqc/RXYLspcBK4Hvzsay6g0PiaP3/JSj9/yUV511RA+Qmdm81lULPiIm8v/fbAh3ImIbcAHwHeB834rMzGxu9fSLThHxcyavpDEzsznknyowM0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwSVTjgJW2QdIeky1uMP0HSVyRtlPSJ4kU0M7MiCgW8pIuA4Yg4Bzhd0oomk70V+ExErAWOkbS2i3KamdkMFW3BTwA35MO3AOc2meZR4LmSjgdOAbYXXJaZmRVQNOAXAQ/lw7uApU2muR04DXgX8ON8uikkrcu7cDbu3LmzYFHMzKyZogG/DxjLhxe3mM9/BC6JiCuAnwD/snGCiFgfEWsjYu34+HjBopiZWTNFA34Tk90yq4CtTaY5AXiepGHgbCAKLsvMzAooGvA3Am+V9BHgd4B7JF3ZMM2fAOuBx4AlwOcKl9LMzGZspMiLImKvpAngAuDqiNgB3N0wzfeA53RdQjMzK6RQwANExG4mr6QxM7OS8TdZzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRBUOeEkbJN0h6fI2010j6TeLLsfMzIopFPCSLgKGI+Ic4HRJK1pM9zLgGRFxUxdlNDOzAoq24CeAG/LhW4BzGyeQtAD4a2CrpN8quBwzMyuoaMAvAh7Kh3cBS5tM8/vAvcDVwIskvbNxAknrJG2UtHHnzp0Fi2JmZs0UDfh9wFg+vLjFfF4ArI+IHcCngVc2ThAR6yNibUSsHR8fL1gUMzNrpmjAb2KyW2YVsLXJNPcDp+fDa4FtBZdlZmYFjBR83Y3AbZKWARcCF0u6MiLqr6jZAFwn6WJgAfDG7opqZmYzUSjgI2KvpAngAuDqvBvm7oZpHgd+u+sSmplZIUVb8ETEbiavpDEzs5LxN1nNzBLlgDczS5QD3swsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54M7NEOeDNzBJVOOAlbZB0h6TL20y3VNIPii7HzMyKKRTwki4ChiPiHOB0SSummfzDwFiR5ZiZWXFFW/ATwA358C3Auc0mknQe8ASwo8X4dZI2Stq4c+fOgkUxM7Nmigb8IuChfHgXsLRxAkmjwAeBy1rNJCLWR8TaiFg7Pj5esChmZtZM0YDfx2S3y+IW87kMuCYi9hRchpmZdaFowG9isltmFbC1yTTnA5dKuhVYLem/F1yWmZkVMFLwdTcCt0laBlwIXCzpyog4fEVNRLy8Nizp1oh4e3dFNTOzmSgU8BGxV9IEcAFwdUTsAO6eZvqJQqUzM7PCirbgiYjdTF5JY2ZmJeNvspqZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZokqHPCSNki6Q9LlLcYfJ+lmSbdI+ntJo8WLaWZmM1Uo4CVdBAxHxDnA6ZJWNJnsLcBHIuLVwA7gtcWLaWZmMzVS8HUTwA358C3AucCW+gki4pq6h+PAw40zkbQOWAdw6qmnFiyKmZk1U7SLZhHwUD68C1jaakJJ5wAnRMSdjeMiYn1ErI2ItePj4wWLYmZmzRRtwe8DxvLhxbSoKCQtAT4GvKHgcszMOhYRREA1giD7X6kGh6rBoUpwqFrNHley5yv59BBUAyIgyJ7Lns8eN19WZ8+1MzY6zBknL575CztQNOA3kXXL3AmsAu5rnCA/qfq3wPsiYlvhEpoNsEo1qEb+V80DJ4KoQuXw81m41MKoFjLTaRUkcXh81A1nYzoJn6mvmSzH4cdx5HPtyjQ579p7y8pXW16tfJ04Ypm1MuXvt0jA9pvUu3kXDfgbgdskLQMuBC6WdGVE1F9R8zZgDfABSR8Aro2I/9VdcS0FEVNbVIcqWehNjq+btnHHn/5hF2VqnG/2RDWgWp1sFWZ/2XuoRkOA54/rx5n1U6GAj4i9kiaAC4CrI2IHcHfDNNcC13ZdwnmkdnhZO7SsRn2w5Iedda3AWstvcjhvAbZdzuGhJs91UM6W85t6eBt1rcYIsjDPg93Meq9oC56I2M3klTTJqdQFZq0PL/s/2YdXG1/foqs/HG88JK3WHSqncGjZqWo12Lx9D1sffYLlJy5i9SnHMzTUw+NSm/e8zWUKB3yZbfnl4zx9sNrRtMHUQ+taYNvsqFaDq27+Mfc/vI8Dh6qMjgxxxsmLef+FZ83LHc56z9vcpCQD/skDFZ48UOl3MQzYvH0P9z+8j/2Hsgp3/6Eq9z+8j83b97DmtBP6XDpLkbe5Sf4tmhaq1eCubbv5wl0Pcte23VTdrC9k66NPcODQ1KOpA4eqbH30iT6VyFLnbW5Ski34bvkQb/YsP3ERoyNDh1tTAKMjQyw/cVEfS2Up8zY3yS34JuoP8YKph3g2M6tPOT77EsehAxBVFuaV5epTju930SxR3uYmOeCb8CHe7BkaEu+/8CwW33sjYz+7jXedt2Lgj4TcfVduKW5zRc3bLprpLqPyId7sGhoSo4/eD4/ez5rT3tvv4nTF3XeDIaVtrhvzMuDb7aS1Q7x7HngEhkdYuGBkyiFeL6+x9fW75eYrNGyQzMuAb7eT1g7x3vFv3kNl8VL+8JJ1h4O2ly04tw7Lb7ruOwe8lc287IPvpI+9dog3tu07h0MfensC1id3y6/WfVfP3XdWVvMy4LvZSXt5AtYnd8vPV2jYIJmXAd/NTtrLFpxbh+XnKzRskMzLgO9mJ+2kcih6GZ1bh4OhVfedHcmXlPbXvDzJCsUvo5ruBCx0d6K03bzNBokvGui/edmC79Z0LbhuT5S6dWip8EUD/eeAn2U+UWqDqBddKd4X+m/edtH0ir8Fa4OmV10pqe4Lg/RlRAf8LGv3Ldh+GqQN0+ZOr76dW+Z9oahBO6/ggJ9lZT1ROmgbph2pVxV0r76dW9Z9oRuD9lMVDvgeKOMPHQ3ahmlT9bKC7mVXShn3hXamq0gH7acqHPDzxKBtmDZVLyvoFLtSimpXkXZbGTarPHrJAZ8Q/wRyunpZQafYlVJUu4q0m8qwVeVx1T97Xs/ejwM+Ed3+BLKVWy9ajvUBPohdKb3QriLtpjJsVXls2rabVT3aDx3wiejmJ5DBV9iUXS9ajmU4wV627a6TirRoZdiq8vjpI/u6L3gLhQNe0gZgJfDliLiy6DQ2Ozo5hG+1YZY5AKZTtnDopV60HGfrBHvRz6GM291sHOm2Wh+tKo/TT1rci7cCFAx4SRcBwxFxjqTrJK2IiC0zncZmTzeH8IN4hU0n4ZBaBTDbLcfZ6L/vJqTLuN11ez5iuvXRqvL4tR6+V0XM/CvJkj4KfDUiviLpYmAsIj4502nqLTntrLjg/dfNuCyb794MwOpVqw8/98T+Q1QiiAj27a/w9MEKRy0YZvHCYaTJD2rLvT8CYMXK5zad93Tju3ltO0VeGxE8sOspntx/EBAaEmMLhjl1yVjb97zz8f08su/AEfMcXzzKSccsnHH5m+lmfTTz+NOHeGjPU9RvvhI86/gxjjlq5PD6eOpghYhsXLP1UdRsv5+abrfZZtqtq17Pu9V8e73d9XIfbDW+k+3y/93/UxgeZdmyZ7J44TAjw0MsGi3eW37DJS/ZFBFrm40rOtdFwEP58C5gTZFpJK0D1gEsfuavFipIfbDX6yTw2n3w043v5rUw/QZU5LWSOHXJGPv2j7L/YIWFTcKh1byPWjCMxBEb5cIFwx2Xud0OMd17KrIzPZ0Hd70I2H+wwjFHjbBvf+VwuNfGPXWwwr79lbbB00m5uvl8W42bjW222bwXLxxmbMHwEZXd4oVTP98in1G7z2G6+Xay3fXrMyq6f7dbH5I4c0WxrCuiaAv+L4DPRcSdeVfMsyPiqplOU2/t2rWxcePGGZelmbu37+H2LY/w0W9umdJlsXBkiHedt6IU3Q6Xvvl1APzlZ784p69tptPD7OmW28v302z8Xdt2T/v5fuGuB/n8pgep37oFvPHXfoWL1vxK4eXOxntqNa7de+pmud12V/WizJ1sd/36jIoqsj6OOWqE5z7ruMLLlDTrLfhNwLnAncAq4L6C0/SMv9jTuVq/43QBUK0GB048g8ripdy1bXff+7Nr/ZmN4VA7GdbunETZ3g/0/lr3NaedMOvbfrvPoV2Z2m13g6ab9dELRQP+RuA2ScuAC4GLJV0ZEZdPM82LuyvqzPiLPTMzXQDUWlr7Vr4ehkf46De3zNrVDu2CttX4duEw3Y7Wy/fTjUHcZrsN6XbbXdkq4XbKVmkVCviI2CtpArgAuDoidgB3t5nmsS7LOiNlq0kHWe1qB0ZGgdm72qFd0LYbP104TLej3bVtd0/eT7cGdZvtxdFBWSvhTvTqaKmIwqduI2I3cEO30/RK2WrSQdarroN2FUe3FUurHa2s3Xdl3Wb70ZLuVaNivkn6m6xlqkkHWa+6DtoFba+CuMxdIWXbZvvVki5rJTxofMs+a6vWdbBwZAiRXRUwG10HtaCtVx+07cYX1av302+1lvZTp7101m67N6UlraE5u69qrz77+SbpFnxZDdrJo3ZdB0XfT7s+5171SZe1K6QbvWpp96slPajnI8rGAT/HBvXkUauug27eT7ug7WUQl60rpFu96rPuV3dWipVwPzjg51hqJ496dSK00/GW6VVLu58t6V5+9oN2FF2UA36OpXbyKLX3M6h61dIuc0u6aEgP6lF0EQ74OVbmKziKSO39DKpetrTLeBTVTUindhQ9HQf8HEvt5FFq72dQlbml3QvdhPR8Oup0wM+x1HbE1N5PzSD20Zaxpd0r3YT0fDrqdMD3QTc7YhmDJ7Vg6baPdrrPqIyf3yDqJqTn01GnA36AzKeTQ/3UzeH/dJ8R4M9vlvhXLDvjgB8g8+nkUD91c/g/3WcE+PObJb38FcuUOOAHyHw6OdRP3Rz+T/cZ1YabjfPnN3PzJaS74d+iGSD+fY650c1v1Uz3Gfnzs7nmFvwAmU8nh/qpm8P/dp+RPz+bS4XuydoLs31P1icPVGZlXmXT7b01rfem+4z8+VmjMt6T1frE/Y7l1+5OU/78bK64D97MLFEOeDOzRCXZRTMyLEZHjuzXbHW6oRpQmYW735iZlUmSAf+cZcVOWFSrQSWCagTVKlQje1ypBIeqQaUaHKpW8/8xOX0+bcTkayKCamSVSkQQtK5gzMx6IcmAL2poSGRXPvdO5BVBMDX4gzhcAdQqiurhSiL7X3su8iOOSjUfPlwp1SqXxmW2KAvRdpr272fq/Jq9N/LnD1WPLJuZ9Y4Dfo5JQofrkPl3eVylGhys5EdBleyIqHZ0VFNfBzRexturCqKxoqqpVaxTKtnq1Ao4q2jDXX1WOg54m1PDQ2J4aLjfxeipZl19jd1+Ux43HIlFk6OwRs1GNzuaqj1f/7gTtdfUzzcOj4uGx61K1Gyek92WtVf5qK53ZhzwkjYAK4EvR8SVLaY5DvgbYBh4AnhTRBzopqBmg2IuuvpS0+kXLqdWYpOvDSbPg9Uqp1r3Zu2cWe2Isfa4vpvzcPdi/XCbck2t4KY+O5NKa7iHX3SbUcBLuggYjohzJF0naUVEbGky6VuAj0TE1yVdC7wW+OIslNfMEiR1FnLNJ3Nl2spMW/ATwA358C3AucARAR8R19Q9HAceLlI4MzMrbtqAl/QJ4My6p14BbMiHdwFr2rz+HOCEiLizxfh1wDqAU089tcMim5lZJ6YN+Ih4R/1jSX8BjOUPFzPNN2ElLQE+BrxhmvmvB9ZD9mNjnRXZzMw6MdOfKthE1i0DsArY2mwiSaPA3wLvi4hthUtnZmaFzTTgbwTeKukjwO8AX5a0UlLj1TRvI+u++YCkWyW9aRbKamZmMzDj34OXdAJwAfDtiNgxWwWZzd+DNzObL2b19+AjYjeTV9KYmVlJ+eeCzcwS5YA3M0tUae7JKmknUPSKm5OAR2axOLPF5epcGcsE5SxXGcsE5SxXGcsEs1uu0yJivNmI0gR8NyRtbHWSoZ9crs6VsUxQznKVsUxQznKVsUwwd+VyF42ZWaIc8GZmiUol4Nf3uwAtuFydK2OZoJzlKmOZoJzlKmOZYI7KlUQfvJmZHSmVFryZGZD90KGkCySd1O+y9JsDvgckjUh6IP8dnlslPa/fZSojSUsl3ZYPP0vSg3XrrOllX/ORpOMk3SzpFkl/L2m0DNtXGYM0/ymVLwEvAr4labwM66pfBr6LppNbCM41SWvIblP43n6XBbIgBT4fES+TtAD4ArAE2BAR1/WpTCcAnwNOjog1+d3ClkbEtf0oT16mI241CVxLn7cvSf8a2FJ3h7RfAIv6uX3ln9+X87+LgfOAP6X/6+oVwP6IuFPSh4GdwJIy7Iv5fvjViHjBXOXWQLfg628hCJwuaUW/y5R7MfAbkr4naYOkvt3cPN8RPwUsyp96J7ApIl4KvFHSMX0qWoUsQPfmj18MvF3SXZKu6lOZareafDWwgyy4+r59RcQ1EfH1/OE4cIj+b1/PB94dEX8MfI0s4Muwrv4hD/eXk7Xin6L/66rmw8DYXObWQAc8zW8hWAbfB86PiBcBC4Bf72NZGoN0gsl19m2gL18CiYi9EfFY3VM3k5XthcA5kp7fhzI1BunvUaLtq3aHNODr9Hn7ahKkr6Ek60rZDV7fBOwGfkAJ9kVJ55EdFe5gDnNr0AN+EfBQPrwLWNrHstT7YUT8Ih/eCPTtyKJJkJZ1nf3fiHg8IipkO2Xf1lldkG6nJOuq7g5pf0BJtq+GIA1Ksq4icynwQ2BZv9dVfgOkDwKX5U/N2T446AG/jw5vITjHrpe0StIw8Hrg7n4XqE5Z19nXJD1T0tHAq4Ef9aMQDUFainXV5A5ppdi+GoL0JZRjXb1X0u/nD48H/qoE6+oy4JqI2JM/nrPtqiw7d1Ed3UKwD64Argc2A3dExDf6XJ56ZV1n/xn4FnAn8FcRcd9cF6BJkJZlXU25QxpwD33evpoE6Z9SjnW1nuyuc98mO1n+cvq/L54PXJp/dquB32SO1tVAX0Uj6VjgNuD/ABcCL27ojrCcpFsjYkLSacBXgG+QtbpenHeLzHuS/hVwFZOtvE8C78bb1xHyk/c3AAvJjrbeR3ZOx+tqGnnIv445yq2BDnjo3S0EUyZpGVkL4mveCafn7atzXledm6t1NfABb2ZmzQ16H7yZmbXggDczS5QD3swsUQ54M7NEOeDNzBL1/wH1b9zOmL3zRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "差分序列的白噪声检验结果为： (array([0.03654365]), array([0.84839715]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DELL\\AppData\\anaconda3\\lib\\site-packages\\statsmodels\\stats\\diagnostic.py:524: FutureWarning: The value returned will change to a single DataFrame after 0.12 is released.  Set return_df to True to use to return a DataFrame now.  Set return_df to False to silence this warning.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 模型检验\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "# 根据定阶结果构建ARIMA模型\n",
    "model = ARIMA(smooth, order=(3, 1, 3))\n",
    "results = model.fit();\n",
    "resid = results.resid;\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40)\n",
    "plt.title('残差自相关图')\n",
    "plt.show()  # 绘制残差的自相关图\n",
    "print('差分序列的白噪声检验结果为：', acorr_ljungbox(resid.values.squeeze(), lags=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4870984  0.02171681 0.2477509  0.14905175 0.14600184 0.19569564\n",
      " 0.2159885  0.0191982  0.27653338 0.13231004 0.14132094 0.30863207\n",
      " 0.32167718 0.16912889 0.02397832 0.0469754  0.50315255 0.04638673\n",
      " 0.26871363 0.11979034 0.19632509 0.03020687 0.45170315 0.73328027\n",
      " 0.08066432 0.10019886 0.14168629 0.22854101 0.10649153 0.02958789\n",
      " 0.17885379]\n",
      "[0, 9.871259096627663, 0.7299498170694667, 5.049649389736282, 5.209281771795838, 2.6976544684858186, 1.8133442054743072, 9.899294071329683, 0.15021438296813283, 5.920955641215485, 5.453809296932777, 0, 0, 4.0050988233674385, 9.843197920823886, 9.407125837035998, 0, 9.42160019760184, 0.2659617826996401, 6.5555216926321025, 2.668448467339688, 9.751924264603044, 0, 0, 8.319722930173192, 7.4909771816427995, 5.434755179038178, 1.3358240205777179, 7.200094197303946, 9.761906299621806, 3.5121028263654708]\n"
     ]
    }
   ],
   "source": [
    "# 获取预测与真实值数据\n",
    "predict_sunspots = results.predict(start=str('2014-08-01'), end=str('2014-08-31'), dynamic=False)\n",
    "right_num=combine['2014-08-01':'2014-08-31']\n",
    "\n",
    "# 预测值和实际值\n",
    "predict_array=predict_sunspots.values.flatten()\n",
    "right_num_array=right_num.values.flatten()\n",
    "\n",
    "# 得到误差\n",
    "error_new=np.abs(np.array(predict_array)-np.array(right_num_array))/np.array(right_num_array).flatten()\n",
    "print(error_new)\n",
    "\n",
    "# 得到得分\n",
    "score_array=[]\n",
    "for i in range(31):\n",
    "    a=0\n",
    "    error=abs(predict_array[i]-right_num_array[i])/right_num_array[i]\n",
    "    if error>=0.3:\n",
    "        a=0\n",
    "    else:\n",
    "        a=5*cos(10*pi*error/3)+5\n",
    "    score_array.append(a)\n",
    "print(score_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制真实值和预测值的对比图\n",
    "fig, ax = plt.subplots(figsize=(7,4 ))\n",
    "combine['2014-08'].plot(ax=ax)\n",
    "predict_sunspots.plot(ax=ax,style='r--')\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.legend(labels=[\"真实值\",\"预测值\"])\n",
    "plt.title('对比图')\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
